Exploring the Potential of Generative Adversarial Networks in Enhancing Urban Renewal Efficiency

Abstract

As Chinese cities transition into a stage of stock development, the revitalization of industrial areas becomes increasingly crucial, serving as a pivotal factor in urban renewal. The renovation of old factory buildings is in full swing, and architects often rely on matured experience to produce several profile renovation schemes for selection during the renovation process. However, when dealing with a large number of factories, this task can consume a significant amount of manpower. In the era of maturing machine learning, this study, set against the backdrop of the renovation of old factory buildings in an industrial district, explores the potential application of deep learning technology in improving the efficiency of factory renovation. We establish a factory renovation profile generation model based on the generative adversarial networks (GANs), learning and generating design features for the renovation of factory building profiles. To ensure a balance between feasibility and creativity in the generated designs, this study employs various transformation techniques on each original profile image during dataset construction, creating mappings between the original profile images and various potential renovation schemes. Additionally, data augmentation techniques are applied to expand the dataset, and the trained models are validated and analyzed on the test set. This study demonstrates the significant potential of the GANs in factory renovation profile design, providing designers with richer reference solutions.